This Month in Tech and Innovation
How has Agile at scale impacted S&P Global? Is the world ready for the fourth industrial revolution? What is the best Sentiment Analysis approach? What is edge computing, and how are 5G networks involved? How are data science and Colorado connected? These questions, and more, are answered in this month's technology and innovation research roundup.
Published: August 1, 2019
Agile at Scale
This adoption, like most big changes, has not been without its challenges. But the lessons learned can help other companies and professionals adopt Agile at scale within their businesses. Embodying transparency, responsiveness, and accountability at every level and in every area of the organization was essential to success.
Key Takeaways
- – Today, S&P Global has over 250 scrum teams operating across the company in both technical and non-technical areas. With 20,000 employees in over 65 locations, the company has adopted Agile at scale in a way few other companies in any industry can claim. By way of comparison, one of S&P Global’s closest competitors recently won an award for standing up 10 scrum teams.
- – Starting from the simple need to integrate technology team after a merger, today the company continues to grow and expand Agile across marketing, human resources and product management, as well as numerous technical teams. This adoption, like most big changes, has not been without its challenges. But the lessons learned can help other companies and professionals adopt Agile at scale within their businesses.
The Fourth Industrial Revolution: Are We Ready?
The Fourth Industrial Revolution describes the next industrial era in which the characteristics of man and machine begin to merge, whereby human capabilities are enhanced by genetic engineering and wearable and implantable technology, and machines acquire human characteristics, particularly cognitive capabilities. Rapid developments in artificial intelligence (AI) and robotics— coupled with ubiquitous connectivity and vast, easily accessible processing power—are laying the groundwork for fundamental structural changes in the global economy. These mutually reinforcing catalysts are driving exponential innovation across a wide swathe of the economy, reshaping entire industries and creating new ones.
Highlights
- – 38% of American workers may need to change occupations by 2030, according to PwC. That means about 45 million people already in the workforce might need to be retrained over the next 11 years.
- – McKinsey Global Institute has estimated that approximately 50% of the activities people are paid to do, representing USD 16 trillion in costs to the global economy, can be automated using currently available technology.
- – This article, along with the latest issue of Indexology® Magazine, provides further explains the implications of these circumstances.
Sentiment Analysis – Is It All The Same?
Sentiment Analysis (SA) also commonly referred to as Opinion Extraction, Opinion Mining, Sentiment Mining, and Subjectivity Analysis looks at the use of natural language processing (NLP) and text analysis techniques to systematically identify, extract, and quantify subjective information and attitudes from different sources. SA initiatives underway at S&P Global Market Intelligence all have different characteristics that reflect their overall use case, available data sources, and chosen methodologies. Following the six-step approach laid out in this article may assist you in framing and scoping any SA you are considering.
Key Takeaways
- – Sentiment can reveal a lot, whether analyzing what a CEO says during quarterly earnings calls, what the social media footprint of a private entity identifies as trends, or the views of employees about a specific company.
- – It is important to remember that not all SA is the same. It is a very broad area and there is much to consider.
- –We identify a six step framework to consider when looking at a potential implementation of SA.
As 5G networks roll, carriers eye edge computing as new service
As wireless operators begin lighting up next-generation 5G networks, the combination of faster mobile broadband speeds coupled with massive modern data loads is expected to drive an emerging technology: edge computing. Edge computing aims to make data processing more efficient by cutting down on the distance that information must travel. The major U.S. wireless carriers are all testing edge processing platforms to compete for this next wave of computing, with demand driven by increased data consumption as consumers, businesses and municipalities embrace a growing number of connected devices, and the internet of things expands.
Highlights
- – The rise of edge computing will not directly threaten existing cloud providers like Amazon.com Inc., Microsoft Corp. and Alphabet Inc., experts agreed. Rather, edge computing is expected to develop alongside the cloud as an option for processing large amounts of data from local devices.
- – Hewlett Packard Enterprise Co. estimates that as much as 75% of enterprise data will be generated and processed at the edge by 2025. The company has committed to a $4 billion investment in edge technologies over the next four years.
Data Science Might Explain Insurtech Startup’s Expansion Strategy
On the face of it, a San Francisco-based insurance technology company starting out in a different state — Colorado — might seem puzzling. But if one digs into data on the Centennial State, it starts to make sense. Colorado had the third-highest percentage increase in private auto direct premiums written in 2018 and has favorable characteristics in terms of population and the amount that people drive. The company, Noblr Inc., offers telematics-based insurance policies, which reward drivers with discounts for safe driving behavior, similar to Ohio-based startup Root Inc. Noblr has its eye on Texas next, based on product filings submitted to state regulators, which also seems like a wise move based on population trends.
Key Takeaways
- – We ran a multiple linear regression, on a national level, using a series of independent variables (vehicle sales, vehicle miles traveled and gas prices) to see if they helped explain private auto premium trends.
- – After a log transformation of the variables and two rounds of differencing to make the time series stationary (i.e. remove the effects of trends and seasonality), the variable that appeared to be most meaningful was vehicle miles traveled.